{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "650d6139-4564-4264-ae46-8fd2b67d38b7",
   "metadata": {},
   "source": [
    "# Quantum Phase Estimation for a Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b06cf9-8c33-4c22-af2f-7cb93af4d68d",
   "metadata": {},
   "source": [
    "This notebook demonstrates the capability of Classiq's Synthesis engine to reduce depth and cx-counts when modeling a Quantum Phase Estimation (QPE) on a unitary that is hard-coded unitary matrix (of the form $e^{2\\pi i A}$, with $A$ Hermitian)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae4eeacb-7974-4070-a437-eeaec6a086b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:09:49.503913Z",
     "iopub.status.busy": "2024-05-07T14:09:49.502863Z",
     "iopub.status.idle": "2024-05-07T14:09:49.717854Z",
     "shell.execute_reply": "2024-05-07T14:09:49.717077Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precisions: [1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "# taking a random example, rescaling and shifting the matrix to guarantee eigenvalues in [0,1)\n",
    "np.random.seed(1235)\n",
    "new_mat = np.random.rand(8, 8)\n",
    "new_mat = (new_mat + new_mat.T) / 2\n",
    "\n",
    "w, v = np.linalg.eig(new_mat)\n",
    "w_max = np.max(np.abs(w))\n",
    "\n",
    "mew_mat = (new_mat + w_max) / (2 * w_max)\n",
    "\n",
    "my_unitary = scipy.linalg.expm(1j * 2 * np.pi * new_mat)\n",
    "\n",
    "precisions = [l for l in range(1, 9)]\n",
    "print(\"precisions:\", precisions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "795812fc-8e0f-4864-9081-12f0794e7ea1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:09:49.722716Z",
     "iopub.status.busy": "2024-05-07T14:09:49.721445Z",
     "iopub.status.idle": "2024-05-07T14:09:49.727242Z",
     "shell.execute_reply": "2024-05-07T14:09:49.726562Z"
    }
   },
   "outputs": [],
   "source": [
    "transpilation_options = {\"classiq\": \"custom\", \"qiskit\": 3}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06cb3c7c-70ae-4b7c-b447-4a910f1487a1",
   "metadata": {},
   "source": [
    "## 1. Classiq's QPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2c41612-1657-4b20-812f-9ee33ddef3e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:09:49.733039Z",
     "iopub.status.busy": "2024-05-07T14:09:49.731317Z",
     "iopub.status.idle": "2024-05-07T14:12:42.538928Z",
     "shell.execute_reply": "2024-05-07T14:12:42.538213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classiq depths: [180, 363, 546, 731, 916, 1101, 1286, 1471]\n",
      "classiq cx-counts: [96, 195, 294, 396, 500, 606, 714, 824]\n"
     ]
    }
   ],
   "source": [
    "from classiq import (\n",
    "    CustomHardwareSettings,\n",
    "    Preferences,\n",
    "    QArray,\n",
    "    QNum,\n",
    "    QuantumProgram,\n",
    "    allocate,\n",
    "    allocate_num,\n",
    "    create_model,\n",
    "    qfunc,\n",
    "    qpe,\n",
    "    set_preferences,\n",
    "    show,\n",
    "    synthesize,\n",
    "    unitary,\n",
    ")\n",
    "\n",
    "classiq_depths = []\n",
    "classiq_cx_counts = []\n",
    "\n",
    "preferences = Preferences(\n",
    "    custom_hardware_settings=CustomHardwareSettings(basis_gates=[\"cx\", \"u\"]),\n",
    "    transpilation_option=transpilation_options[\"classiq\"],\n",
    ")\n",
    "\n",
    "for precision in precisions:\n",
    "\n",
    "    @qfunc\n",
    "    def main():\n",
    "        phase = QNum(\"phase\")\n",
    "        state = QArray(\"state\")\n",
    "        allocate(3, state)\n",
    "        allocate_num(precision, False, precision, phase)\n",
    "        qpe(\n",
    "            unitary=lambda: unitary(elements=my_unitary.tolist(), target=state),\n",
    "            phase=phase,\n",
    "        )\n",
    "\n",
    "    qmod = create_model(main)\n",
    "    qmod = set_preferences(qmod, preferences=preferences)\n",
    "    qprog = synthesize(qmod)\n",
    "    circuit = QuantumProgram.from_qprog(qprog)\n",
    "    depth_classiq = circuit.transpiled_circuit.depth\n",
    "    classiq_depths.append(depth_classiq)\n",
    "    classiq_cx_counts.append(circuit.transpiled_circuit.count_ops[\"cx\"])\n",
    "\n",
    "print(\"classiq depths:\", classiq_depths)\n",
    "print(\"classiq cx-counts:\", classiq_cx_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4942b6-7a59-469c-88e5-9d0dec9f199f",
   "metadata": {},
   "source": [
    "## 2. Comparing to Qiskit Implementations\n",
    "\n",
    "The qiskit data was generated using qiskit version 1.0.0. To run the qiskit code uncomment the commented cells below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca2a77fa-9347-4c7d-b87d-265d6d270f22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:12:42.542619Z",
     "iopub.status.busy": "2024-05-07T14:12:42.541848Z",
     "iopub.status.idle": "2024-05-07T14:12:42.545484Z",
     "shell.execute_reply": "2024-05-07T14:12:42.544883Z"
    }
   },
   "outputs": [],
   "source": [
    "qiskit_depths = [320, 950, 2206, 4706, 9694, 19658, 39574, 79394]\n",
    "qiskit_cx_counts = [162, 484, 1124, 2398, 4938, 10008, 20136, 40378]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d333c51-e1bf-4ef5-a257-08526a87269c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:12:42.547963Z",
     "iopub.status.busy": "2024-05-07T14:12:42.547469Z",
     "iopub.status.idle": "2024-05-07T14:12:42.550625Z",
     "shell.execute_reply": "2024-05-07T14:12:42.550015Z"
    }
   },
   "outputs": [],
   "source": [
    "# from importlib.metadata import version\n",
    "# try:\n",
    "#     import qiskit\n",
    "#     if version('qiskit') != \"1.0.0\":\n",
    "#       !pip uninstall qiskit -y\n",
    "#       !pip install qiskit==1.0.0\n",
    "# except ImportError:\n",
    "#     !pip install qiskit==1.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7072a1bd-8964-4865-87aa-80f55e190e59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:12:42.553015Z",
     "iopub.status.busy": "2024-05-07T14:12:42.552580Z",
     "iopub.status.idle": "2024-05-07T14:12:42.555727Z",
     "shell.execute_reply": "2024-05-07T14:12:42.554992Z"
    }
   },
   "outputs": [],
   "source": [
    "# from qiskit import QuantumCircuit, QuantumRegister, transpile\n",
    "# from qiskit.circuit.library import PhaseEstimation\n",
    "\n",
    "# q = QuantumRegister(3, \"q\")\n",
    "# qc = QuantumCircuit(q)\n",
    "# qc.unitary(my_unitary.tolist(), q)\n",
    "\n",
    "# qiskit_depths = []\n",
    "# qiskit_cx_counts = []\n",
    "# for precision in precisions:\n",
    "#     qpe_qc = PhaseEstimation(precision, qc)\n",
    "#     transpiled_cir = transpile(\n",
    "#         qpe_qc,\n",
    "#         basis_gates=[\"u\", \"cx\"],\n",
    "#         optimization_level=transpilation_options[\"qiskit\"],\n",
    "#     )\n",
    "#     qiskit_depths.append(transpiled_cir.depth())\n",
    "#     qiskit_cx_counts.append(transpiled_cir.count_ops()[\"cx\"])\n",
    "\n",
    "# print(\"qiskit depths:\", qiskit_depths)\n",
    "# print(\"qiskit cx-counts:\", qiskit_cx_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7225939b-3389-4724-a9d7-95db708cead3",
   "metadata": {},
   "source": [
    "## 3. Plotting the Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15318386-56e3-4360-9a7b-8f2c9d0b148f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:12:42.558125Z",
     "iopub.status.busy": "2024-05-07T14:12:42.557774Z",
     "iopub.status.idle": "2024-05-07T14:12:43.337481Z",
     "shell.execute_reply": "2024-05-07T14:12:43.336751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.9, 65000.0, '(a)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "classiq_color = \"#119DA4\"\n",
    "qiskit_color = \"#bb8bff\"\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "plt.rc(\"savefig\", dpi=300)\n",
    "plt.rcParams[\"axes.linewidth\"] = 1\n",
    "plt.rcParams[\"xtick.major.size\"] = 5\n",
    "plt.rcParams[\"xtick.minor.size\"] = 5\n",
    "plt.rcParams[\"ytick.major.size\"] = 5\n",
    "plt.rcParams[\"ytick.minor.size\"] = 5\n",
    "\n",
    "plt.rcParams[\"axes.linewidth\"] = 1\n",
    "plt.rcParams[\"xtick.major.size\"] = 5\n",
    "plt.rcParams[\"xtick.minor.size\"] = 5\n",
    "plt.rcParams[\"ytick.major.size\"] = 5\n",
    "plt.rcParams[\"ytick.minor.size\"] = 5\n",
    "\n",
    "\n",
    "(classiq1,) = plt.semilogy(\n",
    "    precisions,\n",
    "    classiq_depths,\n",
    "    \"-o\",\n",
    "    label=\"classiq depth\",\n",
    "    markerfacecolor=classiq_color,\n",
    "    markeredgecolor=\"k\",\n",
    "    markersize=8,\n",
    "    markeredgewidth=1.5,\n",
    "    linewidth=1.5,\n",
    "    color=classiq_color,\n",
    ")\n",
    "(classiq2,) = plt.semilogy(\n",
    "    precisions,\n",
    "    classiq_cx_counts,\n",
    "    \"-*\",\n",
    "    label=\"classiq cx-counts\",\n",
    "    markerfacecolor=classiq_color,\n",
    "    markeredgecolor=\"k\",\n",
    "    markersize=12,\n",
    "    markeredgewidth=1.5,\n",
    "    linewidth=1.5,\n",
    "    color=classiq_color,\n",
    ")\n",
    "(qiskit1,) = plt.semilogy(\n",
    "    precisions,\n",
    "    qiskit_depths,\n",
    "    \"-s\",\n",
    "    label=\"qiskit depth\",\n",
    "    markerfacecolor=qiskit_color,\n",
    "    markeredgecolor=\"k\",\n",
    "    markersize=7,\n",
    "    markeredgewidth=1.5,\n",
    "    linewidth=1.5,\n",
    "    color=qiskit_color,\n",
    ")\n",
    "(qiskit2,) = plt.semilogy(\n",
    "    precisions,\n",
    "    qiskit_cx_counts,\n",
    "    \"-v\",\n",
    "    label=\"qiskit cx-counts\",\n",
    "    markerfacecolor=qiskit_color,\n",
    "    markeredgecolor=\"k\",\n",
    "    markersize=8,\n",
    "    markeredgewidth=1.5,\n",
    "    linewidth=1.5,\n",
    "    color=qiskit_color,\n",
    ")\n",
    "\n",
    "first_legend = plt.legend(\n",
    "    handles=[qiskit1, qiskit2],\n",
    "    fontsize=16,\n",
    "    loc=\"lower left\",\n",
    "    bbox_to_anchor=(0.1, 0.75),\n",
    ")\n",
    "ax = plt.gca().add_artist(first_legend)\n",
    "plt.legend(handles=[classiq1, classiq2], fontsize=16, loc=\"lower right\")\n",
    "\n",
    "plt.ylabel(\"Depth, CX-counts\", fontsize=16)\n",
    "plt.xlabel(\"QPE-precision\", fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.text(0.9, 0.65e5, \"(a)\", fontsize=16)"
   ]
  }
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